Thermoacoustic imaging is an imaging modality that provides information relating to the thermoelastic properties of tissue. Thermoacoustic imaging uses short pulses of electromagnetic energy, such as radio frequency (RF) pulses, directed into a subject to heat an object (region) of interest within the subject rapidly, which causes the object to expand and then contract, resulting in acoustic pressure waves (signals) being induced in the subject that are detected using an acoustic receiver such as an ultrasound or thermoacoustic transducer array. The detected acoustic pressure waves are analyzed through signal processing, and processed for presentation and interpretation by an operator.
During signal processing of the acoustic pressure waves, sophisticated image reconstruction algorithms are employed that enable thermoacoustic images of the object to be generated by reconstructing heat absorption distribution within the object while reducing noise and other artifacts. Most thermoacoustic image reconstruction algorithms used to generate thermoacoustic images are derived from methods originally developed for other imaging modalities, such as conventional ultrasound and computed tomography (CT). As such, these image reconstruction algorithms are not optimized for processing thermoacoustic image data.
For example, conventional CT reconstruction algorithms are based on the projection-slice (or Fourier slice) theorem, which requires a large number of views or measurements around the object to be imaged. Given that x-rays and acoustic waves behave very differently as they propagate through tissue, conventional CT reconstruction algorithms are too simplistic at best.
Simply stated, each CT view of a given object constitutes a projection of the object along the plane of the transducer array. A Fourier transform of this projection corresponds to an infinitely-thin slice of the object, parallel to the plane. A full reconstruction of the object requires a dense sampling of the Fourier space, and hence a large number of CT views (or projections). In contrast, a single thermoacoustic view packs far more complex information than a simple projection of the object along the plane. Truncating the thermoacoustic view to a simple projection, followed by a dense sampling of views is highly inefficient.
In particular, one known image reconstruction algorithm involves deconvolving transducer array time-series data to obtain heat absorption projection data, filtering the projection data to reduce blurring effects using, for example, a Shepp-Logan or similar filter, and performing back-projections over all transducer elements of the transducer array to reconstruct the thermoacoustic image. One problem with this known image reconstruction algorithm however, is that time-domain filters, such as the Shepp-Logan filter, assume that the thermoacoustic data is complete i.e. that the thermoacoustic data comprises a large set of image views. As mentioned above, this assumption may not hold for thermoacoustic imaging, where a small number of views may be sufficient for reconstructing the thermoacoustic image. As such, applying filters such as the Shepp-Logan filter, may result in severe artifacts, thereby degrading the quality of the reconstructed thermoacoustic image.
As will be appreciated, improved techniques for reconstructing thermoacoustic images are desired. It is therefore an object at least to provide a novel method and system for reconstructing a thermoacoustic image.